Energy & Utilities Built for the Intelligence Age
Grid resilience through predictive AI.
Asset health monitoring that prevents outages.
Data platforms ready for the energy transition.
All built on Ithara. All code yours to keep.
The Vision
The Agentic Enterprise
in Energy & Utilities
What does it look like when AI agents work alongside grid operators, field crews, and asset managers?
Not automation that replaces expertise. Agents that analyze sensor data so operators focus on critical decisions. That predict equipment failures so maintenance is proactive, not reactive. That unify decades of siloed data so your teams see the full picture.
Outage prevention through AI-driven asset health. Field operations streamlined. Grid modernization accelerated.
All built on Ithara. All code you own.
The Reality You're Living
What We Hear From Energy & Utility Leaders
"Our data lives across SCADA, AMI, GIS, asset systems, weather feeds, and vendor platforms. We don't have a unified, trusted view of grid, asset, and customer data."
"We've built promising ML models, but performance degrades quickly due to sparse historical data, inconsistent labeling, and limited feedback loops from operations."
"Programs like wildfire mitigation, DER integration, grid resilience, and electrification require rapid innovation—but our delivery cycles are measured in years, not months."
"We're accountable for safety, reliability, and compliance—but we lack continuous, auditable visibility across assets, field operations, and risk indicators."
Sound familiar?
AI/Agent Engineering
Intelligent Systems That Transform Grid Operations
Secure Field Asset Data Intake
The Problem
Utility asset imagery from multiple vendors submitted through fragmented, manual processes—creating data security risks, inconsistent metadata, verification bottlenecks, and systems that cannot scale with growing field documentation volumes.
What We Build
A secure, cloud-native intake and ingestion platform with a unified vendor portal, automated image and metadata validation, strong encryption and access controls, and a scalable architecture designed for high-volume, multi-vendor submissions.
Your Experts
Operations and compliance teams focus on exception handling, governance, and regulatory oversight while the platform automates secure ingestion, validation, and auditability at scale.
Outcomes
95% reduction
Data processing time
80% decrease
Vendor submission errors
100%
Audit trail coverage
3–5× increase
Daily image ingestion
AI-Driven Grid Resilience
The Problem
Utility maintenance is reactive and manual—asset issues identified only after failures occur, inspections time-intensive and inconsistent, early warning signs missed, and maintenance response delayed due to manual workflows.
What We Build
An AI-powered predictive maintenance platform that analyzes field asset imagery and operational data to detect anomalies, assess asset health, generate early warnings, and automatically create prioritized maintenance tickets integrated with existing work management systems.
Your Experts
Maintenance engineers and operations teams focus on high-risk assets and informed decision-making while the platform continuously monitors asset health, surfaces early warnings, and automates routine maintenance workflows.
Outcomes
30% reduction
Unplanned outages
25% decrease
Maintenance costs
2–3× faster
Issue detection
40% reduction
Field inspection effort
Wildfire Risk Intelligence
The Problem
Wildfire risk assessment relies on periodic manual inspections, static vegetation management schedules, and disconnected data sources—leaving utilities exposed to catastrophic events and regulatory penalties.
What We Build
An integrated risk platform that analyzes vegetation encroachment, asset proximity, real-time weather data, LIDAR scans, and historical imagery to identify high-risk corridors and support Public Safety Power Shutoff (PSPS) decisions.
Your Experts
Wildfire mitigation teams and grid operators make informed decisions on vegetation management priorities, circuit de-energization, and resource deployment based on AI-driven risk scoring.
Outcomes
Continuous
Risk monitoring
Prioritized
Vegetation management
Reduced
PSPS scope & duration
Regulatory
Compliance documentation
Data Intelligence
Get Legacy Systems Ready for AI
The Data Problem in Utilities
Large utilities operate with decades of fragmented data across operational, customer, financial, and asset systems—making it difficult to scale analytics, control costs, or support grid modernization:
- • Legacy infrastructure constraints limiting scalability as data volumes grow
- • Siloed data across SCADA, AMI, GIS, SAP, Oracle, and specialized utility systems
- • High cost and complexity of maintaining on-premise data platforms
- • Slow analytics cycles, with complex data requests taking weeks or months
What Ithara Brings
Proven blueprints, accelerators, and cloud-native frameworks for modernizing utility data platforms—enabling scalable ingestion from legacy and enterprise systems, unified analytics across structured and unstructured data, and governance-ready architectures that support regulatory, operational, and AI-driven use cases.
What We Build
Unified Customer Records
A consolidated, analytics-ready view of customer, billing, usage, and service interactions across systems.
Modern, Scalable Data Lake
A cloud-native platform combining Snowflake and Databricks to support enterprise analytics, AI/ML, and regulatory reporting at scale.
Research & Advanced Analytics Platform
A flexible environment for data science, machine learning, and grid innovation use cases including predictive maintenance and wildfire mitigation.
Unstructured Data Hub
Centralized processing and analytics for documents, images, LIDAR, and geospatial data to support asset intelligence and compliance.
Typical Outcomes
70%+ reduction
Analytics turnaround time
30-50% lower
Infrastructure costs
10× faster
Data ingestion speed
Months, not years
AI readiness
AI Ops
Operate AI Systems at Production Scale
Why This Matters for Utilities
Utilities operating hybrid cloud and ML platforms struggle to balance speed, scale, and governance—resulting in slow environment provisioning, inconsistent configurations, and operational risk:
- • Manual infrastructure provisioning causing weeks-long delays for ML teams
- • Inconsistent configurations and security gaps from manual setup
- • Bottlenecks in ITIL-driven workflows slowing cloud adoption
- • Limited visibility across hybrid cloud and on-prem environments
What We Build
Model Monitoring
Continuous monitoring of ML models for performance, drift, and operational health across environments.
Governance Framework
Policy-driven controls ensuring security, compliance, auditability, and ITIL alignment across ML and cloud workflows.
Automated Infrastructure Provisioning
AI-driven provisioning of compute, storage, networking, and ML infrastructure with standardized, secure configurations.
Explainability Layer
Transparent visibility into model behavior, infrastructure decisions, and automation actions to support trust and compliance.
FinOps Enablement
Cost visibility, allocation, and optimization across cloud and ML workloads to manage spend and maximize ROI.
Typical Outcomes
Weeks → Hours
Infrastructure provisioning
60%+ reduction
Manual IT operations
100%
Configuration consistency
FinOps visibility
Cost control
Pre-Built on Ithara
Energy & Utility Accelerators
These aren't demos. They're production-tested components your pod customizes for your environment.
| Agent/Component | Function |
|---|---|
| Secure Field Asset Data Intake Agent | Ingests asset imagery and field data from multiple vendors through a secure, unified portal; automates validation, metadata extraction, and audit-ready tracking |
| Grid Resilience & Asset Health Agent | Continuously analyzes asset imagery and operational data to detect anomalies, assess asset health, and generate early warnings for potential failures |
| Predictive Maintenance Agent | Identifies failure patterns across historical and real-time data, prioritizes maintenance actions, and automatically generates work orders |
| Wildfire Risk Detection Agent | Analyzes vegetation, asset proximity, weather, and imagery data to identify wildfire risks and support mitigation planning |
| Utility Data Lake Modernization Framework | Cloud-native blueprints, ingestion frameworks, and data models to unify enterprise, operational, and geospatial data |
| Unstructured Data Processing Hub | Scalable processing and analytics for images, documents, LIDAR, and geospatial data used in inspections and compliance |
| Infrastructure Automation & MLOps Agent | Automates provisioning of hybrid cloud and ML environments with ITIL-compliant workflows and GPU orchestration |
| Model Monitoring & Explainability Layer | Monitors model performance, drift, and decisions while providing explainability and compliance visibility |
| Utility Governance & Compliance Framework | Embeds regulatory, security, and data governance controls aligned with utility and state requirements |
| FinOps Optimization Component | Cost visibility, allocation, and optimization across cloud and analytics workloads |
How We Engage
Innovation Streaming
Everything runs in 6-week cycles with 2-week cooldowns. Each cycle delivers production-ready capability—not a report, not a roadmap, working systems that generate value.
New clients start with discovery on us. No charge, no obligation. For the right engagements, we offer gain-share options. All solutions built on Ithara. All code yours to keep.
See how we engage6 weeks
To production-ready outcomes
3 seniors
Dedicated expert pod
100%
Your code to keep
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